ⓘ Global Data Lab publishes its own subnational regions, which match national admin units in some countries and group several together in others. The level shown is the nearest administrative tier, not a claim that these are that tier.
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BGD, BGD, BGD, BGD, BGD |
region_code |
Admin code | string | SEL | 100% | - | - |
region_name |
Admin name | string | SEL | 0% | 24 | Total, Bagerhat, Khulna, Satkhira, Bandarban, Cox s... |
human_development_index |
Human development index | float | SEL | 0% | 77 | 0.418, 0.422, 0.388, 0.462, 0.439 |
health_index |
Health index | float | SEL | 0% | 71 | 0.551, 0.568, 0.553, 0.573, 0.565 |
education_index |
Education index | float | SEL | 0% | 84 | 0.308, 0.311, 0.279, 0.429, 0.338 |
income_index |
Income index | float | SEL | 0% | 70 | 0.431, 0.425, 0.379, 0.4, 0.444 |
life_expectancy |
Life expectancy | float | SEL | 0% | 97 | 55.82, 56.9, 55.92, 57.22, 56.72 |
mean_years_schooling |
Mean years schooling | float | SEL | 0% | 94 | 4.617, 4.571, 3.812, 7.578, 4.996 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
year |
year | integer | 0% | 5 | 1990, 1990, 1990, 1990, 1990 |
level |
level | string | 0% | 2 | national, subnational, subnational, subnational, subnational |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
pcode |
Admin code | string | SEL | 0% | 1 | BD |
region_name |
Region name | string | SEL | 0% | 1 | Bangladesh |
F_TL |
Female population | integer | SEL | 0% | 1 | 84329234 |
M_TL |
Male population | integer | SEL | 0% | 1 | 81321241 |
T_TL |
Total population | integer | SEL | 0% | 1 | 165650475 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
admin_level |
Admin level | string | 0% | 1 | admin_0 |
year |
Reference year | integer | 0% | 1 | 2022 |
year |
year | integer | 0% | 1 | 2022 |
F_00_04 |
Female population age 0-4 | integer | 0% | 1 | 6975963 |
F_05_09 |
Female population age 5-9 | integer | 0% | 1 | 6958005 |
F_10_14 |
Female population age 10-14 | integer | 0% | 1 | 6971312 |
F_15_19 |
Female population age 15-19 | integer | 0% | 1 | 7080764 |
F_20_24 |
Female population age 20-24 | integer | 0% | 1 | 7518766 |
F_25_29 |
Female population age 25-29 | integer | 0% | 1 | 7306642 |
F_30_34 |
Female population age 30-34 | integer | 0% | 1 | 6818916 |
F_35_39 |
Female population age 35-39 | integer | 0% | 1 | 6300444 |
F_40_44 |
Female population age 40-44 | integer | 0% | 1 | 5635072 |
F_45_49 |
Female population age 45-49 | integer | 0% | 1 | 5272957 |
F_50_54 |
Female population age 50-54 | integer | 0% | 1 | 4474340 |
F_55_59 |
Female population age 55-59 | integer | 0% | 1 | 3629720 |
F_60_64 |
Female population age 60-64 | integer | 0% | 1 | 2946743 |
F_65_69 |
Female population age 65-69 | integer | 0% | 1 | 2356037 |
F_70_74 |
Female population age 70-74 | integer | 0% | 1 | 1762433 |
F_75_79 |
Female population age 75-79 | integer | 0% | 1 | 1201011 |
F_80Plus |
F_80Plus | integer | 0% | 1 | 1120109 |
M_00_04 |
Male population age 0-4 | integer | 0% | 1 | 7213458 |
M_05_09 |
Male population age 5-9 | integer | 0% | 1 | 7190658 |
M_10_14 |
Male population age 10-14 | integer | 0% | 1 | 7244774 |
M_15_19 |
Male population age 15-19 | integer | 0% | 1 | 7316623 |
M_20_24 |
Male population age 20-24 | integer | 0% | 1 | 7532560 |
M_25_29 |
Male population age 25-29 | integer | 0% | 1 | 6959030 |
M_30_34 |
Male population age 30-34 | integer | 0% | 1 | 6239317 |
M_35_39 |
Male population age 35-39 | integer | 0% | 1 | 5759245 |
M_40_44 |
Male population age 40-44 | integer | 0% | 1 | 5121028 |
M_45_49 |
Male population age 45-49 | integer | 0% | 1 | 4671126 |
| +24 more pending fields — download the CSV/Parquet to see them all. | |||||
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
pcode |
Admin code | string | SEL | 0% | 8 | BD10, BD20, BD30, BD40, BD45 |
region_name |
Region name | string | SEL | 0% | 8 | Barisal, Chittagong, Dhaka, Khulna, Mymensingh |
F_TL |
Female population | integer | SEL | 0% | 8 | 4243339, 16951397, 23170810, 8305656, 6126785 |
M_TL |
Male population | integer | SEL | 0% | 8 | 3867384, 15641888, 24142104, 7826836, 5682977 |
T_TL |
Total population | integer | SEL | 0% | 8 | 8110723, 32593285, 47312914, 16132492, 11809762 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
admin_level |
Admin level | string | 0% | 1 | admin_1, admin_1, admin_1, admin_1, admin_1 |
year |
Reference year | integer | 0% | 1 | 2022, 2022, 2022, 2022, 2022 |
year |
year | integer | 0% | 1 | 2022, 2022, 2022, 2022, 2022 |
F_00_04 |
Female population age 0-4 | integer | 0% | 8 | 339426, 1540556, 1806957, 568414, 566549 |
F_05_09 |
Female population age 5-9 | integer | 0% | 8 | 362038, 1537534, 1764824, 583437, 576681 |
F_10_14 |
Female population age 10-14 | integer | 0% | 8 | 374056, 1576126, 1831215, 639865, 504068 |
F_15_19 |
Female population age 15-19 | integer | 0% | 8 | 320435, 1635132, 2123745, 623928, 411677 |
F_20_24 |
Female population age 20-24 | integer | 0% | 8 | 322732, 1500941, 2415955, 685682, 469176 |
F_25_29 |
Female population age 25-29 | integer | 0% | 8 | 335313, 1392138, 2241383, 700953, 480218 |
F_30_34 |
Female population age 30-34 | integer | 0% | 8 | 333002, 1253442, 1969502, 709907, 472535 |
F_35_39 |
Female population age 35-39 | integer | 0% | 8 | 305773, 1158126, 1759748, 673633, 440366 |
F_40_44 |
Female population age 40-44 | integer | 0% | 8 | 272321, 1029059, 1542934, 601479, 418247 |
F_45_49 |
Female population age 45-49 | integer | 0% | 8 | 275481, 981521, 1402539, 577532, 381084 |
F_50_54 |
Female population age 50-54 | integer | 0% | 8 | 248877, 851430, 1167551, 462629, 351341 |
F_55_59 |
Female population age 55-59 | integer | 0% | 8 | 210364, 676109, 903021, 408984, 268402 |
F_60_64 |
Female population age 60-64 | integer | 0% | 8 | 174948, 576853, 727334, 309526, 242370 |
F_65_69 |
Female population age 65-69 | integer | 0% | 8 | 137293, 434469, 559974, 285894, 189003 |
F_70_74 |
Female population age 70-74 | integer | 0% | 8 | 99870, 350446, 413186, 197172, 157168 |
F_75_79 |
Female population age 75-79 | integer | 0% | 8 | 67697, 226665, 280347, 153386, 96420 |
F_80Plus |
F_80Plus | integer | 0% | 8 | 63713, 230850, 260595, 123235, 101480 |
M_00_04 |
Male population age 0-4 | integer | 0% | 8 | 346661, 1593555, 1873027, 588651, 582713 |
M_05_09 |
Male population age 5-9 | integer | 0% | 8 | 369598, 1587527, 1827080, 598125, 597789 |
M_10_14 |
Male population age 10-14 | integer | 0% | 8 | 390709, 1595834, 1889931, 672314, 538121 |
M_15_19 |
Male population age 15-19 | integer | 0% | 8 | 327535, 1578968, 2156257, 662315, 450737 |
M_20_24 |
Male population age 20-24 | integer | 0% | 8 | 275687, 1439157, 2711565, 646823, 413897 |
M_25_29 |
Male population age 25-29 | integer | 0% | 8 | 266840, 1197104, 2520626, 628911, 407972 |
M_30_34 |
Male population age 30-34 | integer | 0% | 8 | 266209, 1041947, 2133910, 611870, 390548 |
M_35_39 |
Male population age 35-39 | integer | 0% | 8 | 258444, 968348, 1850851, 584220, 376766 |
M_40_44 |
Male population age 40-44 | integer | 0% | 8 | 234792, 849176, 1557561, 552110, 358577 |
M_45_49 |
Male population age 45-49 | integer | 0% | 8 | 221704, 788655, 1361313, 530032, 325226 |
| +24 more pending fields — download the CSV/Parquet to see them all. | |||||
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
pcode |
Admin code | string | SEL | 0% | 100 | BD1004, BD1006, BD1009, BD1042, BD1078 |
region_name |
Region name | string | SEL | 0% | 100 | Barguna, Barisal, Bhola, Jhalokati, Patuakhali |
F_TL |
Female population | integer | SEL | 0% | 100 | 472401, 1150901, 911898, 341741, 811884 |
M_TL |
Male population | integer | SEL | 0% | 100 | 425887, 1042061, 860217, 297582, 736425 |
T_TL |
Total population | integer | SEL | 0% | 100 | 898288, 2192962, 1772115, 639323, 1548309 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
admin_level |
Admin level | string | 0% | 2 | admin_2, admin_2, admin_2, admin_2, admin_2 |
year |
Reference year | integer | 0% | 1 | 2022, 2022, 2022, 2022, 2022 |
year |
year | integer | 0% | 1 | 2022, 2022, 2022, 2022, 2022 |
F_00_04 |
Female population age 0-4 | integer | 0% | 100 | 35243, 86494, 88464, 23474, 65245 |
F_05_09 |
Female population age 5-9 | integer | 0% | 100 | 36903, 93527, 92700, 26352, 69952 |
F_10_14 |
Female population age 10-14 | integer | 0% | 100 | 36318, 104748, 88771, 30296, 67898 |
F_15_19 |
Female population age 15-19 | integer | 0% | 100 | 29058, 94742, 72461, 26609, 55569 |
F_20_24 |
Female population age 20-24 | integer | 0% | 100 | 35736, 86918, 74857, 23364, 61455 |
F_25_29 |
Female population age 25-29 | integer | 0% | 100 | 40703, 86272, 75048, 24296, 67193 |
F_30_34 |
Female population age 30-34 | integer | 0% | 99 | 39913, 88311, 68839, 26804, 65835 |
F_35_39 |
Female population age 35-39 | integer | 0% | 100 | 36859, 84150, 59103, 25600, 58630 |
F_40_44 |
Female population age 40-44 | integer | 0% | 100 | 31880, 75809, 51228, 24038, 52059 |
F_45_49 |
Female population age 45-49 | integer | 0% | 100 | 33303, 76594, 50187, 23663, 53398 |
F_50_54 |
Female population age 50-54 | integer | 0% | 99 | 29875, 68343, 46670, 20974, 49934 |
F_55_59 |
Female population age 55-59 | integer | 0% | 100 | 24799, 54741, 42855, 17718, 40910 |
F_60_64 |
Female population age 60-64 | integer | 0% | 100 | 19901, 48261, 34176, 15212, 34122 |
F_65_69 |
Female population age 65-69 | integer | 0% | 100 | 15191, 36207, 26867, 12279, 26091 |
F_70_74 |
Female population age 70-74 | integer | 0% | 100 | 11307, 28464, 17491, 9242, 19031 |
F_75_79 |
Female population age 75-79 | integer | 0% | 100 | 7919, 18448, 11825, 6174, 12383 |
F_80Plus |
F_80Plus | integer | 0% | 100 | 7493, 18872, 10356, 5646, 12179 |
M_00_04 |
Male population age 0-4 | integer | 0% | 100 | 36007, 87872, 91269, 23827, 66584 |
M_05_09 |
Male population age 5-9 | integer | 0% | 100 | 37269, 96163, 95022, 26869, 70897 |
M_10_14 |
Male population age 10-14 | integer | 0% | 100 | 39499, 106311, 93166, 30735, 73233 |
M_15_19 |
Male population age 15-19 | integer | 0% | 100 | 31180, 94439, 75213, 26027, 58186 |
M_20_24 |
Male population age 20-24 | integer | 0% | 100 | 28301, 78946, 61180, 20015, 50000 |
M_25_29 |
Male population age 25-29 | integer | 0% | 100 | 30768, 69933, 63119, 17614, 51958 |
M_30_34 |
Male population age 30-34 | integer | 0% | 100 | 32452, 67550, 61051, 17685, 54056 |
M_35_39 |
Male population age 35-39 | integer | 0% | 100 | 31577, 66842, 56430, 18525, 52094 |
M_40_44 |
Male population age 40-44 | integer | 0% | 100 | 27326, 64055, 47275, 18993, 44936 |
M_45_49 |
Male population age 45-49 | integer | 0% | 100 | 25949, 62127, 40729, 19125, 41464 |
| +24 more pending fields — download the CSV/Parquet to see them all. | |||||
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BGD |
admin_level |
Admin level | string | SEL | 0% | 1 | admin_0 |
admin_name |
Admin name | string | SEL | 0% | 1 | Bangledesh |
admin_code |
Admin code | string | SEL | 0% | 1 | 71232402B16306086515200 |
area_sqkm |
Area sqkm | float | SEL | 0% | 1 | 140227.6407 |
pop_2024 |
Population count | integer | SEL | 0% | 1 | 167324511 |
pop_density_2024 |
Population density | float | SEL | 0% | 1 | 1193.23 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BGD, BGD, BGD, BGD, BGD |
admin_level |
Admin level | string | SEL | 0% | 1 | admin_1, admin_1, admin_1, admin_1, admin_1 |
admin_name |
Admin name | string | SEL | 0% | 8 | Dhaka, Chittagong, Rajshani, Rangpur, Khulna |
admin_code |
Admin code | string | SEL | 0% | 8 | 32408957B77718829177955, 32408957B94023193097291,... |
area_sqkm |
Area sqkm | float | SEL | 0% | 8 | 20924.9447, 30503.9625, 18518.8221, 16567.8141, 21489.2058 |
pop_2024 |
Population count | integer | SEL | 0% | 8 | 42695069, 33369497, 21451474, 18746815, 18108258 |
pop_density_2024 |
Population density | float | SEL | 0% | 8 | 2040.39, 1093.94, 1158.36, 1131.52, 842.67 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BGD, BGD, BGD, BGD, BGD |
admin_level |
Admin level | string | SEL | 0% | 1 | locality, locality, locality, locality, locality |
admin_name |
Admin name | string | SEL | 0% | 99 | Dhaka, Chattogram, Savar, Brahmanbaria, Kishoreganj |
admin_code |
Admin code | integer | SEL | 0% | 100 | 6090, 7439, 6000, 6663, 6297 |
area_sqkm |
Area sqkm | float | SEL | 0% | 100 | 6580.6935, 516.5304, 589.3416, 554.5021, 591.3683 |
pop_2024 |
Population count | integer | SEL | 0% | 100 | 28040936, 4014973, 2397824, 1659451, 1174942 |
pop_density_2024 |
Population density | float | SEL | 0% | 100 | 4261.09, 7772.97, 4068.65, 2992.69, 1986.82 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
ghs_pop_2025 |
ghs_pop_2025 | integer | 0% | 100 | 37307160, 5175717, 3695797, 1945179, 1511954 |
landscan_vs_ghs_ratio |
landscan_vs_ghs_ratio | float | 0% | 95 | 0.752, 0.776, 0.649, 0.853, 0.777 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
city_name |
Admin name | string | SEL | 0% | 99 | Dhaka, Chattogram, Savar, Brahmanbaria, Kishoreganj |
country_code |
Country code | string | SEL | 0% | 1 | BGD, BGD, BGD, BGD, BGD |
population |
Population count | integer | SEL | 0% | 100 | 37307160, 5175717, 3695797, 1945179, 1511954 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
city_id |
city_id | integer | 0% | 100 | 6090, 7439, 6000, 6663, 6297 |
name_alternates |
name_alternates | string | 100% | - | - |
country_name |
country_name | string | 0% | 1 | Bangladesh, Bangladesh, Bangladesh, Bangladesh, Bangladesh |
population_year |
population_year | integer | 0% | 1 | 2025, 2025, 2025, 2025, 2025 |
latitude |
latitude | string | 100% | - | - |
longitude |
longitude | string | 100% | - | - |
region |
region | string | 100% | - | - |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BD, BD, BD, BD, BD |
population_count |
Population count | float | SEL | 2% | 65 | 51828660.0, 53310348.0, 54881146.0, 56504402.0, 58178374.0 |
life_expectancy |
Life expectancy | float | SEL | 3% | 64 | 43.98, 44.887, 45.765, 45.722, 46.752 |
gdp_per_capita_usd |
Gdp per capita usd | float | SEL | 2% | 65 | 82.4812774116354, 90.3685786294248, 92.5894211769315,... |
literacy_rate_pct |
Literacy rate percent | float | SEL | 77% | 15 | 29.2299995422363, 35.3199996948242, 47.4900016784668,... |
under5_mortality_per_1000 |
Under5 mortality per 1000 | float | SEL | 3% | 63 | 261.7, 255.1, 248.8, 242.9, 237.7 |
poverty_headcount_pct |
Poverty headcount percent | float | SEL | 89% | 7 | 56.6, 50.1, 48.9, 40.0, 31.5 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
country_name |
country_name | string | 0% | 1 | Bangladesh, Bangladesh, Bangladesh, Bangladesh, Bangladesh |
year |
year | integer | 0% | 66 | 1960, 1961, 1962, 1963, 1964 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BD, BD, BD, BD, BD |
life_expectancy |
Life expectancy | float | SEL | 3% | 64 | 43.98, 44.887, 45.765, 45.722, 46.752 |
infant_mortality_per_1000 |
Infant mortality per 1000 | float | SEL | 3% | 61 | 102.5, 100.6, 98.8, 97.1, 95.5 |
under5_mortality_per_1000 |
Under5 mortality per 1000 | float | SEL | 3% | 63 | 261.7, 255.1, 248.8, 242.9, 237.7 |
maternal_mortality_per_100k |
Maternal mortality per 100k | float | SEL | 41% | 39 | 926.0, 891.0, 884.0, 881.0, 877.0 |
fertility_rate |
Fertility rate | float | SEL | 3% | 62 | 6.742, 6.78, 6.806, 6.798, 6.785 |
birth_rate_per_1000 |
Birth rate per 1000 | float | SEL | 3% | 64 | 48.999, 49.131, 49.102, 48.74, 48.363 |
death_rate_per_1000 |
Death rate per 1000 | float | SEL | 3% | 64 | 21.624, 20.909, 20.239, 20.198, 19.373 |
physicians_per_1000 |
Physicians per 1000 | float | SEL | 53% | 31 | 0.123, 0.119, 0.12, 0.154, 0.144 |
hospital_beds_per_1000 |
Hospital beds per 1000 | float | SEL | 62% | 25 | 0.155100002884865, 0.215800002217293, 0.225199997425079,... |
vaccination_rate_pct |
Vaccination rate percent | float | SEL | 33% | 23 | 1.0, 1.0, 1.0, 1.0, 2.0 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
country_name |
country_name | string | 0% | 1 | Bangladesh, Bangladesh, Bangladesh, Bangladesh, Bangladesh |
year |
year | integer | 0% | 66 | 1960, 1961, 1962, 1963, 1964 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 100% | - | - |
women_who_are_literate |
Women who are literate | float | CCL | 0% | 43 | 57.3, 67.3, 69.6, 79.0, 77.5 |
men_who_are_literate |
Men who are literate | float | CCL | 66% | 16 | 59.1, 61.6, 60.1, 57.7, 51.4 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
country_name |
country_name | string | 0% | 1 | Bangladesh, Bangladesh, Bangladesh, Bangladesh, Bangladesh |
survey_year |
survey_year | integer | 0% | 5 | 2007, 2011, 2014, 2017, 2022 |
region |
region | string | 0% | 11 | ..Chattogram, ..Chattogram, ..Chattogram, ..Chattogram,... |
survey_id |
survey_id | string | 0% | 5 | BD2007DHS, BD2011DHS, BD2014DHS, BD2017DHS, BD2022DHS |
survey_type |
survey_type | string | 0% | 1 | DHS, DHS, DHS, DHS, DHS |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 100% | - | - |
infant_mortality_per_1000 |
Infant mortality per 1000 | float | SEL | 0% | 47 | 103.0, 77.0, 69.0, 67.0, 54.0 |
under5_mortality_per_1000 |
Under5 mortality per 1000 | float | SEL | 0% | 53 | 167.0, 131.0, 110.0, 103.0, 79.0 |
vaccination_rate_pct |
Vaccination rate percent | float | SEL | 15% | 60 | 53.7, 51.3, 68.4, 75.1, 77.2 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
country_name |
country_name | string | 0% | 1 | Bangladesh, Bangladesh, Bangladesh, Bangladesh, Bangladesh |
survey_year |
survey_year | integer | 0% | 9 | 1994, 1997, 2000, 2004, 2007 |
region |
region | string | 0% | 11 | ..Chattogram, ..Chattogram, ..Chattogram, ..Chattogram,... |
survey_id |
survey_id | string | 0% | 9 | BD1994DHS, BD1997DHS, BD2000DHS, BD2004DHS, BD2007DHS |
survey_type |
survey_type | string | 0% | 1 | DHS, DHS, DHS, DHS, DHS |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_of_origin_iso |
Country of origin iso | string | CCL | 0% | 1 | -, -, -, -, - |
refugees |
Refugees | integer | CCL | 0% | 75 | 2116011, 1952928, 1847304, 1749628, 1717966 |
idps |
Idps | integer | CCL | 0% | 34 | 0, 0, 0, 0, 0 |
stateless |
Stateless | integer | CCL | 0% | 23 | 0, 0, 0, 0, 0 |
others_of_concern |
Others of concern | integer | CCL | 0% | 30 | 0, 0, 0, 0, 0 |
total_population |
Total population | string | CCL | 100% | - | - |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
year |
year | integer | 0% | 75 | 1951, 1952, 1953, 1954, 1955 |
country_of_origin |
country_of_origin | string | 100% | - | - |
country_of_asylum |
country_of_asylum | string | 100% | - | - |
country_of_asylum_iso |
country_of_asylum_iso | string | 0% | 1 | -, -, -, -, - |
population_type |
population_type | string | 100% | - | - |
asylum_seekers |
asylum_seekers | integer | 0% | 34 | 0, 0, 0, 0, 0 |
female_total |
female_total | string | 100% | - | - |
male_total |
male_total | string | 100% | - | - |
female_0_4 |
female_0_4 | string | 100% | - | - |
female_5_11 |
female_5_11 | string | 100% | - | - |
female_12_17 |
female_12_17 | string | 100% | - | - |
female_18_59 |
female_18_59 | string | 100% | - | - |
female_60_plus |
female_60_plus | string | 100% | - | - |
male_0_4 |
male_0_4 | string | 100% | - | - |
male_5_11 |
male_5_11 | string | 100% | - | - |
male_12_17 |
male_12_17 | string | 100% | - | - |
male_18_59 |
male_18_59 | string | 100% | - | - |
male_60_plus |
male_60_plus | string | 100% | - | - |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_of_origin_iso |
Country of origin iso | string | CCL | 0% | 1 | -, -, -, -, - |
refugees |
Refugees | integer | CCL | 0% | 75 | 2116011, 1952928, 1847304, 1749628, 1717966 |
idps |
Idps | integer | CCL | 0% | 34 | 0, 0, 0, 0, 0 |
stateless |
Stateless | integer | CCL | 0% | 23 | 0, 0, 0, 0, 0 |
others_of_concern |
Others of concern | integer | CCL | 0% | 30 | 0, 0, 0, 0, 0 |
total_population |
Total population | string | CCL | 100% | - | - |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
year |
year | integer | 0% | 75 | 1951, 1952, 1953, 1954, 1955 |
country_of_origin |
country_of_origin | string | 100% | - | - |
country_of_asylum |
country_of_asylum | string | 100% | - | - |
country_of_asylum_iso |
country_of_asylum_iso | string | 0% | 1 | -, -, -, -, - |
population_type |
population_type | string | 100% | - | - |
asylum_seekers |
asylum_seekers | integer | 0% | 34 | 0, 0, 0, 0, 0 |
female_total |
female_total | string | 100% | - | - |
male_total |
male_total | string | 100% | - | - |
female_0_4 |
female_0_4 | string | 100% | - | - |
female_5_11 |
female_5_11 | string | 100% | - | - |
female_12_17 |
female_12_17 | string | 100% | - | - |
female_18_59 |
female_18_59 | string | 100% | - | - |
female_60_plus |
female_60_plus | string | 100% | - | - |
male_0_4 |
male_0_4 | string | 100% | - | - |
male_5_11 |
male_5_11 | string | 100% | - | - |
male_12_17 |
male_12_17 | string | 100% | - | - |
male_18_59 |
male_18_59 | string | 100% | - | - |
male_60_plus |
male_60_plus | string | 100% | - | - |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
glottocode |
Glottocode | string | CCL | 0% | 50 | asho1236, assa1263, aton1241, bawm1236, beng1280 |
name |
Name | string | CCL | 0% | 50 | Asho Chin, Assamese, Atong (India), Bawm Chin, Bengali |
iso639_3 |
Iso639 3 | string | CCL | 4% | 48 | csh, asm, aot, bgr, ben |
level |
Level | string | CCL | 0% | 1 | language, language, language, language, language |
family_name |
Family name | string | CCL | 100% | - | - |
family_glottocode |
Family glottocode | string | CCL | 0% | 5 | sino1245, indo1319, sino1245, sino1245, indo1319 |
parent_name |
Parent name | string | CCL | 100% | - | - |
parent_glottocode |
Parent glottocode | string | CCL | 0% | 32 | asho1237, assa1262, koch1249, fala1242, gaud1238 |
endangerment_status |
Endangerment status | string | CCL | 100% | - | - |
country_codes |
Country codes | string | SEL+ | 0% | 10 | ['BD', 'MM'], ['BD', 'BT', 'IN'], ['BD', 'IN'], ['BD',... |
child_family_count |
Child family count | integer | CCL | 0% | 1 | 0, 0, 0, 0, 0 |
child_language_count |
Child language count | integer | CCL | 0% | 1 | 0, 0, 0, 0, 0 |
child_dialect_count |
Child dialect count | integer | CCL | 0% | 12 | 7, 5, 0, 0, 15 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
latitude |
latitude | float | 0% | 49 | 21.393, 26.0876, 25.346508, 22.4405233333, 24.0 |
longitude |
longitude | float | 0% | 50 | 93.5069, 91.2932, 90.658722, 92.92553, 90.0 |
classification |
classification | string | 0% | 1 | [], [], [], [], [] |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
telephones_fixed_lines_subscriptions_per_100_inhabitants_numeric |
Fixed line subscriptions per 100 | float | SEL | 0% | 1 | 2024.0 |
telephones_mobile_cellular_subscriptions_per_100_inhabitants_numeric |
Mobile subscriptions per 100 | float | SEL | 0% | 1 | 108.0 |
internet_country_code_text |
Internet country code text | string | SEL+ | 0% | 1 | .bd |
internet_users_percent_of_population_numeric |
Internet penetration percent | float | SEL | 0% | 1 | 45.0 |
broadband_fixed_subscriptions_subscriptions_per_100_inhabitants_numeric |
Broadband subscriptions per 100 | float | SEL | 0% | 1 | 8.0 |
country_code |
Country code | string | SEL | 0% | 1 | BGD |
country_name |
Country name | string | SEL | 0% | 1 | Bangladesh |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
telephones_fixed_lines_total_subscriptions_text |
telephones_fixed_lines_total_subscriptions_text | string | 0% | 1 | 285,000 (2024 est.) |
telephones_fixed_lines_total_subscriptions_numeric |
telephones_fixed_lines_total_subscriptions_numeric | float | 0% | 1 | 285000.0 |
telephones_fixed_lines_subscriptions_per_100_inhabitants_text |
telephones_fixed_lines_subscriptions_per_100_inhabitants_text | string | 0% | 1 | (2024 est.) less than 1 |
telephones_mobile_cellular_total_subscriptions_text |
telephones_mobile_cellular_total_subscriptions_text | string | 0% | 1 | 188 million (2024 est.) |
telephones_mobile_cellular_total_subscriptions_numeric |
telephones_mobile_cellular_total_subscriptions_numeric | float | 0% | 1 | 188.0 |
telephones_mobile_cellular_subscriptions_per_100_inhabitants_text |
telephones_mobile_cellular_subscriptions_per_100_inhabitants_text | string | 0% | 1 | 108 (2024 est.) |
broadcast_media_text |
broadcast_media_text | string | 0% | 1 | state-owned Bangladesh Television (BTV) broadcasts... |
broadcast_media_numeric |
broadcast_media_numeric | float | 0% | 1 | 41.0 |
internet_users_percent_of_population_text |
internet_users_percent_of_population_text | string | 0% | 1 | 45% (2023 est.) |
broadband_fixed_subscriptions_total_text |
broadband_fixed_subscriptions_total_text | string | 0% | 1 | 12.9 million (2023 est.) |
broadband_fixed_subscriptions_total_numeric |
broadband_fixed_subscriptions_total_numeric | float | 0% | 1 | 12.9 |
broadband_fixed_subscriptions_subscriptions_per_100_inhabitants_text |
broadband_fixed_subscriptions_subscriptions_per_100_inhabitants_text | string | 0% | 1 | 8 (2023 est.) |
source_section |
source_section | string | 0% | 1 | Communications |
source_profile_path |
source_profile_path | string | 0% | 1 | south-asia/bg.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
real_gdp_per_capita_real_gdp_per_capita_2024_numeric |
Real gdp per capita 2024 (numeric) | float | SEL | 0% | 1 | 8500.0 |
gdp_official_exchange_rate_numeric |
Gdp total usd | float | SEL | 0% | 1 | 450.119 |
population_below_poverty_line_numeric |
Poverty headcount percent | float | SEL | 0% | 1 | 18.7 |
country_code |
Country code | string | SEL | 0% | 1 | BGD |
country_name |
Country name | string | SEL | 0% | 1 | Bangladesh |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
economic_overview_text |
economic_overview_text | string | 0% | 1 | one of the fastest growing emerging market economies;... |
economic_overview_numeric |
economic_overview_numeric | float | 0% | 1 | -19.0 |
real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2024_text |
Real gdp purchasing power parity 2024 (text) | string | 0% | 1 | $1.473 trillion (2024 est.) |
real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2024_numeric |
Real gdp purchasing power parity 2024 (numeric) | float | 0% | 1 | 1.473 |
real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2023_text |
Real gdp purchasing power parity 2023 (text) | string | 0% | 1 | $1.413 trillion (2023 est.) |
real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2023_numeric |
Real gdp purchasing power parity 2023 (numeric) | float | 0% | 1 | 1.413 |
real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2022_text |
Real gdp purchasing power parity 2022 (text) | string | 0% | 1 | $1.336 trillion (2022 est.) |
real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2022_numeric |
Real gdp purchasing power parity 2022 (numeric) | float | 0% | 1 | 1.336 |
real_gdp_purchasing_power_parity_note |
real_gdp_purchasing_power_parity_note | string | 0% | 1 | note: data in 2021 dollars |
real_gdp_growth_rate_real_gdp_growth_rate_2024_text |
Real gdp growth rate 2024 (text) | string | 0% | 1 | 4.2% (2024 est.) |
real_gdp_growth_rate_real_gdp_growth_rate_2024_numeric |
Real gdp growth rate 2024 (numeric) | float | 0% | 1 | 4.2 |
real_gdp_growth_rate_real_gdp_growth_rate_2023_text |
Real gdp growth rate 2023 (text) | string | 0% | 1 | 5.8% (2023 est.) |
real_gdp_growth_rate_real_gdp_growth_rate_2023_numeric |
Real gdp growth rate 2023 (numeric) | float | 0% | 1 | 5.8 |
real_gdp_growth_rate_real_gdp_growth_rate_2022_text |
Real gdp growth rate 2022 (text) | string | 0% | 1 | 7.1% (2022 est.) |
real_gdp_growth_rate_real_gdp_growth_rate_2022_numeric |
Real gdp growth rate 2022 (numeric) | float | 0% | 1 | 7.1 |
real_gdp_growth_rate_note |
real_gdp_growth_rate_note | string | 0% | 1 | note: annual GDP % growth based on constant local currency |
real_gdp_per_capita_real_gdp_per_capita_2024_text |
Real gdp per capita 2024 (text) | string | 0% | 1 | $8,500 (2024 est.) |
real_gdp_per_capita_real_gdp_per_capita_2023_text |
Real gdp per capita 2023 (text) | string | 0% | 1 | $8,200 (2023 est.) |
real_gdp_per_capita_real_gdp_per_capita_2023_numeric |
Real gdp per capita 2023 (numeric) | float | 0% | 1 | 8200.0 |
real_gdp_per_capita_real_gdp_per_capita_2022_text |
Real gdp per capita 2022 (text) | string | 0% | 1 | $7,900 (2022 est.) |
real_gdp_per_capita_real_gdp_per_capita_2022_numeric |
Real gdp per capita 2022 (numeric) | float | 0% | 1 | 7900.0 |
real_gdp_per_capita_note |
real_gdp_per_capita_note | string | 0% | 1 | note: data in 2021 dollars |
gdp_official_exchange_rate_text |
gdp_official_exchange_rate_text | string | 0% | 1 | $450.119 billion (2024 est.) |
inflation_rate_consumer_prices_inflation_rate_consumer_prices_2024_text |
Inflation rate consumer prices 2024 (text) | string | 0% | 1 | 10.5% (2024 est.) |
inflation_rate_consumer_prices_inflation_rate_consumer_prices_2024_numeric |
Inflation rate consumer prices 2024 (numeric) | float | 0% | 1 | 10.5 |
inflation_rate_consumer_prices_inflation_rate_consumer_prices_2023_text |
Inflation rate consumer prices 2023 (text) | string | 0% | 1 | 9.9% (2023 est.) |
inflation_rate_consumer_prices_inflation_rate_consumer_prices_2023_numeric |
Inflation rate consumer prices 2023 (numeric) | float | 0% | 1 | 9.9 |
inflation_rate_consumer_prices_inflation_rate_consumer_prices_2022_text |
Inflation rate consumer prices 2022 (text) | string | 0% | 1 | 7.7% (2022 est.) |
inflation_rate_consumer_prices_inflation_rate_consumer_prices_2022_numeric |
Inflation rate consumer prices 2022 (numeric) | float | 0% | 1 | 7.7 |
inflation_rate_consumer_prices_note |
inflation_rate_consumer_prices_note | string | 0% | 1 | note: annual % change based on consumer prices |
| +122 more extension fields — download the CSV/Parquet to see them all. | |||||
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
electricity_access_electrification_total_population_numeric |
Electricity access percent | float | SEL | 0% | 1 | 99.4 |
country_code |
Country code | string | SEL | 0% | 1 | BGD |
country_name |
Country name | string | SEL | 0% | 1 | Bangladesh |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
electricity_access_electrification_total_population_text |
electricity_access_electrification_total_population_text | string | 0% | 1 | 99.4% (2022 est.) |
electricity_access_electrification_urban_areas_text |
electricity_access_electrification_urban_areas_text | string | 0% | 1 | 100% |
electricity_access_electrification_urban_areas_numeric |
electricity_access_electrification_urban_areas_numeric | float | 0% | 1 | 100.0 |
electricity_access_electrification_rural_areas_text |
electricity_access_electrification_rural_areas_text | string | 0% | 1 | 99.3% |
electricity_access_electrification_rural_areas_numeric |
electricity_access_electrification_rural_areas_numeric | float | 0% | 1 | 99.3 |
electricity_installed_generating_capacity_text |
electricity_installed_generating_capacity_text | string | 0% | 1 | 22.699 million kW (2023 est.) |
electricity_installed_generating_capacity_numeric |
electricity_installed_generating_capacity_numeric | float | 0% | 1 | 22.699 |
electricity_consumption_text |
electricity_consumption_text | string | 0% | 1 | 107.285 billion kWh (2023 est.) |
electricity_consumption_numeric |
electricity_consumption_numeric | float | 0% | 1 | 107.285 |
electricity_imports_text |
electricity_imports_text | string | 0% | 1 | 9.407 billion kWh (2023 est.) |
electricity_imports_numeric |
electricity_imports_numeric | float | 0% | 1 | 9.407 |
electricity_transmission_distribution_losses_text |
electricity_transmission_distribution_losses_text | string | 0% | 1 | 8.279 billion kWh (2023 est.) |
electricity_transmission_distribution_losses_numeric |
electricity_transmission_distribution_losses_numeric | float | 0% | 1 | 8.279 |
electricity_generation_sources_fossil_fuels_text |
electricity_generation_sources_fossil_fuels_text | string | 0% | 1 | 98.4% of total installed capacity (2023 est.) |
electricity_generation_sources_fossil_fuels_numeric |
electricity_generation_sources_fossil_fuels_numeric | float | 0% | 1 | 98.4 |
electricity_generation_sources_solar_text |
electricity_generation_sources_solar_text | string | 0% | 1 | 1% of total installed capacity (2023 est.) |
electricity_generation_sources_solar_numeric |
electricity_generation_sources_solar_numeric | float | 0% | 1 | 1.0 |
electricity_generation_sources_hydroelectricity_text |
electricity_generation_sources_hydroelectricity_text | string | 0% | 1 | 0.6% of total installed capacity (2023 est.) |
electricity_generation_sources_hydroelectricity_numeric |
electricity_generation_sources_hydroelectricity_numeric | float | 0% | 1 | 0.6 |
nuclear_energy_number_of_nuclear_reactors_under_construction_text |
nuclear_energy_number_of_nuclear_reactors_under_construction_text | string | 0% | 1 | 2 (2025) |
nuclear_energy_number_of_nuclear_reactors_under_construction_numeric |
nuclear_energy_number_of_nuclear_reactors_under_construction_numeric | float | 0% | 1 | 2.0 |
coal_production_text |
coal_production_text | string | 0% | 1 | 767,000 metric tons (2023 est.) |
coal_production_numeric |
coal_production_numeric | float | 0% | 1 | 767000.0 |
coal_consumption_text |
coal_consumption_text | string | 0% | 1 | 14.05 million metric tons (2023 est.) |
coal_consumption_numeric |
coal_consumption_numeric | float | 0% | 1 | 14.05 |
coal_imports_text |
coal_imports_text | string | 0% | 1 | 13.305 million metric tons (2023 est.) |
coal_imports_numeric |
coal_imports_numeric | float | 0% | 1 | 13.305 |
coal_proven_reserves_text |
coal_proven_reserves_text | string | 0% | 1 | 3.26 billion metric tons (2023 est.) |
coal_proven_reserves_numeric |
coal_proven_reserves_numeric | float | 0% | 1 | 3.26 |
petroleum_total_petroleum_production_text |
petroleum_total_petroleum_production_text | string | 0% | 1 | 13,000 bbl/day (2023 est.) |
| +17 more extension fields — download the CSV/Parquet to see them all. | |||||
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
land_use_agricultural_land_numeric |
Agricultural land percent | float | SEL | 0% | 1 | 72.3 |
land_use_forest_numeric |
Forest area percent | float | SEL | 0% | 1 | 14.4 |
urbanization_urban_population_numeric |
Urban population percent | float | SEL | 0% | 1 | 40.5 |
urbanization_rate_of_urbanization_numeric |
Urban growth rate percent | float | SEL | 0% | 1 | 2.88 |
waste_and_recycling_municipal_solid_waste_generated_annually_numeric |
Municipal waste kg per capita | float | SEL | 0% | 1 | 14.778 |
country_code |
Country code | string | SEL | 0% | 1 | BGD |
country_name |
Country name | string | SEL | 0% | 1 | Bangladesh |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
environmental_issues_text |
environmental_issues_text | string | 0% | 1 | flooding; water pollution, especially of fishing areas,... |
international_environmental_agreements_party_to_text |
international_environmental_agreements_party_to_text | string | 0% | 1 | Biodiversity, Climate Change, Climate Change-Kyoto... |
international_environmental_agreements_signed_but_not_ratified_text |
international_environmental_agreements_signed_but_not_ratified_text | string | 0% | 1 | none of the selected agreements |
climate_text |
climate_text | string | 0% | 1 | tropical; mild winter (October to March); hot, humid... |
land_use_agricultural_land_text |
land_use_agricultural_land_text | string | 0% | 1 | 72.3% (2023 est.) |
land_use_agricultural_land_arable_land_text |
land_use_agricultural_land_arable_land_text | string | 0% | 1 | arable land: 60.6% (2023 est.) |
land_use_agricultural_land_arable_land_numeric |
land_use_agricultural_land_arable_land_numeric | float | 0% | 1 | 60.6 |
land_use_agricultural_land_permanent_crops_text |
land_use_agricultural_land_permanent_crops_text | string | 0% | 1 | permanent crops: 7.1% (2023 est.) |
land_use_agricultural_land_permanent_crops_numeric |
land_use_agricultural_land_permanent_crops_numeric | float | 0% | 1 | 7.1 |
land_use_agricultural_land_permanent_pasture_text |
land_use_agricultural_land_permanent_pasture_text | string | 0% | 1 | permanent pasture: 4.6% (2023 est.) |
land_use_agricultural_land_permanent_pasture_numeric |
land_use_agricultural_land_permanent_pasture_numeric | float | 0% | 1 | 4.6 |
land_use_forest_text |
land_use_forest_text | string | 0% | 1 | 14.4% (2023 est.) |
land_use_other_text |
land_use_other_text | string | 0% | 1 | 13.3% (2023 est.) |
land_use_other_numeric |
land_use_other_numeric | float | 0% | 1 | 13.3 |
urbanization_urban_population_text |
urbanization_urban_population_text | string | 0% | 1 | 40.5% of total population (2023) |
urbanization_rate_of_urbanization_text |
urbanization_rate_of_urbanization_text | string | 0% | 1 | 2.88% annual rate of change (2020-25 est.) |
carbon_dioxide_emissions_total_emissions_text |
carbon_dioxide_emissions_total_emissions_text | string | 0% | 1 | 125.956 million metric tonnes of CO2 (2023 est.) |
carbon_dioxide_emissions_total_emissions_numeric |
carbon_dioxide_emissions_total_emissions_numeric | float | 0% | 1 | 125.956 |
carbon_dioxide_emissions_from_coal_and_metallurgical_coke_text |
carbon_dioxide_emissions_from_coal_and_metallurgical_coke_text | string | 0% | 1 | 26.967 million metric tonnes of CO2 (2023 est.) |
carbon_dioxide_emissions_from_coal_and_metallurgical_coke_numeric |
carbon_dioxide_emissions_from_coal_and_metallurgical_coke_numeric | float | 0% | 1 | 26.967 |
carbon_dioxide_emissions_from_petroleum_and_other_liquids_text |
carbon_dioxide_emissions_from_petroleum_and_other_liquids_text | string | 0% | 1 | 42.083 million metric tonnes of CO2 (2023 est.) |
carbon_dioxide_emissions_from_petroleum_and_other_liquids_numeric |
carbon_dioxide_emissions_from_petroleum_and_other_liquids_numeric | float | 0% | 1 | 42.083 |
carbon_dioxide_emissions_from_consumed_natural_gas_text |
carbon_dioxide_emissions_from_consumed_natural_gas_text | string | 0% | 1 | 56.906 million metric tonnes of CO2 (2023 est.) |
carbon_dioxide_emissions_from_consumed_natural_gas_numeric |
carbon_dioxide_emissions_from_consumed_natural_gas_numeric | float | 0% | 1 | 56.906 |
particulate_matter_emissions_text |
particulate_matter_emissions_text | string | 0% | 1 | 42.5 micrograms per cubic meter (2019 est.) |
particulate_matter_emissions_numeric |
particulate_matter_emissions_numeric | float | 0% | 1 | 42.5 |
methane_emissions_energy_text |
methane_emissions_energy_text | string | 0% | 1 | 544 kt (2022-2024 est.) |
methane_emissions_energy_numeric |
methane_emissions_energy_numeric | float | 0% | 1 | 544.0 |
methane_emissions_agriculture_text |
methane_emissions_agriculture_text | string | 0% | 1 | 2,391.4 kt (2019-2021 est.) |
methane_emissions_agriculture_numeric |
methane_emissions_agriculture_numeric | float | 0% | 1 | 2391.4 |
| +17 more extension fields — download the CSV/Parquet to see them all. | |||||
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BGD |
country_name |
Country name | string | SEL | 0% | 1 | Bangladesh |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
nationality_noun_text |
nationality_noun_text | string | 0% | 1 | Bangladeshi(s) |
nationality_adjective_text |
nationality_adjective_text | string | 0% | 1 | Bangladeshi |
ethnic_groups_text |
ethnic_groups_text | string | 0% | 1 | Bengali at least 99%, other indigenous ethnic groups 1%... |
ethnic_groups_numeric |
ethnic_groups_numeric | float | 0% | 1 | 99.0 |
source_section |
source_section | string | 0% | 1 | People and Society:ethnic_groups |
source_profile_path |
source_profile_path | string | 0% | 1 | south-asia/bg.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
area_total_numeric |
Area sqkm | float | SEL | 0% | 1 | 148460.0 |
area_land_numeric |
Area land sqkm | float | SEL | 0% | 1 | 130170.0 |
area_water_numeric |
Area water sqkm | float | SEL | 0% | 1 | 18290.0 |
land_boundaries_total_numeric |
Land boundary km | float | SEL | 0% | 1 | 4413.0 |
coastline_numeric |
Coastline km | float | SEL | 0% | 1 | 580.0 |
elevation_highest_point_numeric |
Elevation max m | float | SEL | 0% | 1 | 1060.0 |
elevation_lowest_point_numeric |
Elevation min m | float | SEL | 0% | 1 | 0.0 |
land_use_agricultural_land_numeric |
Agricultural land percent | float | SEL | 0% | 1 | 72.3 |
land_use_forest_numeric |
Forest area percent | float | SEL | 0% | 1 | 14.4 |
irrigated_land_numeric |
Irrigated land sqkm | float | SEL | 0% | 1 | 83690.0 |
country_code |
Country code | string | SEL | 0% | 1 | BGD |
country_name |
Country name | string | SEL | 0% | 1 | Bangladesh |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
location_text |
location_text | string | 0% | 1 | Southern Asia, bordering the Bay of Bengal, between... |
geographic_coordinates_text |
geographic_coordinates_text | string | 0% | 1 | 24 00 N, 90 00 E |
geographic_coordinates_numeric |
geographic_coordinates_numeric | float | 0% | 1 | 24.0 |
map_references_text |
map_references_text | string | 0% | 1 | Asia |
area_total_text |
area_total_text | string | 0% | 1 | 148,460 sq km |
area_land_text |
area_land_text | string | 0% | 1 | 130,170 sq km |
area_water_text |
area_water_text | string | 0% | 1 | 18,290 sq km |
area_comparative_text |
area_comparative_text | string | 0% | 1 | slightly larger than Pennsylvania and New Jersey... |
land_boundaries_total_text |
land_boundaries_total_text | string | 0% | 1 | 4,413 km |
land_boundaries_border_countries_text |
land_boundaries_border_countries_text | string | 0% | 1 | Burma 271 km; India 4,142 km |
land_boundaries_border_countries_numeric |
land_boundaries_border_countries_numeric | float | 0% | 1 | 271.0 |
coastline_text |
coastline_text | string | 0% | 1 | 580 km |
maritime_claims_territorial_sea_text |
maritime_claims_territorial_sea_text | string | 0% | 1 | 12 nm |
maritime_claims_territorial_sea_numeric |
maritime_claims_territorial_sea_numeric | float | 0% | 1 | 12.0 |
maritime_claims_contiguous_zone_text |
maritime_claims_contiguous_zone_text | string | 0% | 1 | 18 nm |
maritime_claims_contiguous_zone_numeric |
maritime_claims_contiguous_zone_numeric | float | 0% | 1 | 18.0 |
maritime_claims_exclusive_economic_zone_text |
maritime_claims_exclusive_economic_zone_text | string | 0% | 1 | 200 nm |
maritime_claims_exclusive_economic_zone_numeric |
maritime_claims_exclusive_economic_zone_numeric | float | 0% | 1 | 200.0 |
maritime_claims_continental_shelf_text |
maritime_claims_continental_shelf_text | string | 0% | 1 | to the outer limits of the continental margin |
climate_text |
climate_text | string | 0% | 1 | tropical; mild winter (October to March); hot, humid... |
terrain_text |
terrain_text | string | 0% | 1 | mostly flat alluvial plain; hilly in southeast |
elevation_highest_point_text |
elevation_highest_point_text | string | 0% | 1 | Mowdok Taung 1,060 m |
elevation_lowest_point_text |
elevation_lowest_point_text | string | 0% | 1 | Bay of Bengal 0 m |
elevation_mean_elevation_text |
elevation_mean_elevation_text | string | 0% | 1 | 85 m |
elevation_mean_elevation_numeric |
elevation_mean_elevation_numeric | float | 0% | 1 | 85.0 |
natural_resources_text |
natural_resources_text | string | 0% | 1 | natural gas, arable land, timber, coal |
land_use_agricultural_land_text |
land_use_agricultural_land_text | string | 0% | 1 | 72.3% (2023 est.) |
land_use_agricultural_land_arable_land_text |
land_use_agricultural_land_arable_land_text | string | 0% | 1 | arable land: 60.6% (2023 est.) |
land_use_agricultural_land_arable_land_numeric |
land_use_agricultural_land_arable_land_numeric | float | 0% | 1 | 60.6 |
land_use_agricultural_land_permanent_crops_text |
land_use_agricultural_land_permanent_crops_text | string | 0% | 1 | permanent crops: 7.1% (2023 est.) |
| +16 more extension fields — download the CSV/Parquet to see them all. | |||||
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BGD |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
country_name_conventional_long_form_text |
country_name_conventional_long_form_text | string | 0% | 1 | People's Republic of Bangladesh |
country_name_conventional_short_form_text |
country_name_conventional_short_form_text | string | 0% | 1 | Bangladesh |
country_name_local_long_form_text |
country_name_local_long_form_text | string | 0% | 1 | Gana Prajatantri Bangladesh |
country_name_local_short_form_text |
country_name_local_short_form_text | string | 0% | 1 | Bangladesh |
country_name_former_text |
country_name_former_text | string | 0% | 1 | East Bengal, East Pakistan |
country_name_etymology_text |
country_name_etymology_text | string | 0% | 1 | the name is a compound of the Bengali words Bangla... |
government_type_text |
government_type_text | string | 0% | 1 | parliamentary republic |
capital_name_text |
capital_name_text | string | 0% | 1 | Dhaka |
capital_geographic_coordinates_text |
capital_geographic_coordinates_text | string | 0% | 1 | 23 43 N, 90 24 E |
capital_geographic_coordinates_numeric |
capital_geographic_coordinates_numeric | float | 0% | 1 | 23.0 |
capital_time_difference_text |
capital_time_difference_text | string | 0% | 1 | UTC+6 (11 hours ahead of Washington, DC, during Standard Time) |
capital_time_difference_numeric |
capital_time_difference_numeric | float | 0% | 1 | 6.0 |
capital_etymology_text |
capital_etymology_text | string | 0% | 1 | the origins of the name are unclear, but it may be... |
administrative_divisions_text |
administrative_divisions_text | string | 0% | 1 | 8 divisions; Barishal, Chattogram, Dhaka, Khulna,... |
administrative_divisions_numeric |
administrative_divisions_numeric | float | 0% | 1 | 8.0 |
legal_system_text |
legal_system_text | string | 0% | 1 | common law, incorporating elements of English common... |
constitution_history_text |
constitution_history_text | string | 0% | 1 | previous 1935, 1956, 1962 (pre-independence); latest... |
constitution_history_numeric |
constitution_history_numeric | float | 0% | 1 | 1935.0 |
constitution_amendment_process_text |
constitution_amendment_process_text | string | 0% | 1 | proposed by the House of the Nation; approval requires... |
international_law_organization_participation_text |
international_law_organization_participation_text | string | 0% | 1 | has not submitted an ICJ jurisdiction declaration;... |
citizenship_citizenship_by_birth_text |
Citizenship by birth (text) | string | 0% | 1 | no |
citizenship_citizenship_by_descent_only_text |
Citizenship by descent only (text) | string | 0% | 1 | at least one parent must be a citizen of Bangladesh |
citizenship_dual_citizenship_recognized_text |
citizenship_dual_citizenship_recognized_text | string | 0% | 1 | yes, but limited to select countries |
citizenship_residency_requirement_for_naturalization_text |
citizenship_residency_requirement_for_naturalization_text | string | 0% | 1 | 5 years |
citizenship_residency_requirement_for_naturalization_numeric |
citizenship_residency_requirement_for_naturalization_numeric | float | 0% | 1 | 5.0 |
suffrage_text |
suffrage_text | string | 0% | 1 | 18 years of age; universal |
suffrage_numeric |
suffrage_numeric | float | 0% | 1 | 18.0 |
executive_branch_chief_of_state_text |
executive_branch_chief_of_state_text | string | 0% | 1 | President Mohammad SHAHABUDDIN Chuppi (since 24 April 2023) |
executive_branch_chief_of_state_numeric |
executive_branch_chief_of_state_numeric | float | 0% | 1 | 24.0 |
executive_branch_head_of_government_text |
executive_branch_head_of_government_text | string | 0% | 1 | Interim Prime Minister Muhammad YUNUS (since 8 August 2024) |
| +61 more extension fields — download the CSV/Parquet to see them all. | |||||
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BGD |
country_name |
Country name | string | SEL | 0% | 1 | Bangladesh |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
background_text |
background_text | string | 0% | 1 | The huge delta region at the confluence of the Ganges... |
background_numeric |
background_numeric | float | 0% | 1 | 10.0 |
source_section |
source_section | string | 0% | 1 | Introduction |
source_profile_path |
source_profile_path | string | 0% | 1 | south-asia/bg.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BGD |
country_name |
Country name | string | SEL | 0% | 1 | Bangladesh |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
languages_languages_text |
Languages (text) | string | 0% | 1 | Bangla 98.8% (official, also known as Bengali), other... |
languages_languages_numeric |
Languages (numeric) | float | 0% | 1 | 98.8 |
languages_major_language_sample_s_text |
languages_major_language_sample_s_text | string | 0% | 1 | বিশ্ব ফ্যাক্টবুক, মৌলিক তথ্যের অপরিহার্য উৎস (Bangla)... |
source_section |
source_section | string | 0% | 1 | People and Society:languages |
source_profile_path |
source_profile_path | string | 0% | 1 | south-asia/bg.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BGD |
country_name |
Country name | string | SEL | 0% | 1 | Bangladesh |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
refugees_and_internally_displaced_persons_refugees_text |
refugees_and_internally_displaced_persons_refugees_text | string | 0% | 1 | 1,005,637 (2024 est.) |
refugees_and_internally_displaced_persons_refugees_numeric |
refugees_and_internally_displaced_persons_refugees_numeric | float | 0% | 1 | 1005637.0 |
refugees_and_internally_displaced_persons_idps_text |
refugees_and_internally_displaced_persons_idps_text | string | 0% | 1 | 756,743 (2024 est.) |
refugees_and_internally_displaced_persons_idps_numeric |
refugees_and_internally_displaced_persons_idps_numeric | float | 0% | 1 | 756743.0 |
refugees_and_internally_displaced_persons_stateless_persons_text |
refugees_and_internally_displaced_persons_stateless_persons_text | string | 0% | 1 | 1,005,520 (2024 est.) |
refugees_and_internally_displaced_persons_stateless_persons_numeric |
refugees_and_internally_displaced_persons_stateless_persons_numeric | float | 0% | 1 | 1005520.0 |
source_section |
source_section | string | 0% | 1 | Transnational Issues:migration |
source_profile_path |
source_profile_path | string | 0% | 1 | south-asia/bg.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BGD |
country_name |
Country name | string | SEL | 0% | 1 | Bangladesh |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
military_and_security_forces_text |
military_and_security_forces_text | string | 0% | 1 | Armed Forces of Bangladesh (aka Bangladesh Defense... |
military_and_security_forces_numeric |
military_and_security_forces_numeric | float | 0% | 1 | 2025.0 |
military_expenditures_military_expenditures_2024_text |
Military expenditures 2024 (text) | string | 0% | 1 | 0.9% of GDP (2024 est.) |
military_expenditures_military_expenditures_2024_numeric |
Military expenditures 2024 (numeric) | float | 0% | 1 | 0.9 |
military_expenditures_military_expenditures_2023_text |
Military expenditures 2023 (text) | string | 0% | 1 | 1% of GDP (2023 est.) |
military_expenditures_military_expenditures_2023_numeric |
Military expenditures 2023 (numeric) | float | 0% | 1 | 1.0 |
military_expenditures_military_expenditures_2022_text |
Military expenditures 2022 (text) | string | 0% | 1 | 1.1% of GDP (2022 est.) |
military_expenditures_military_expenditures_2022_numeric |
Military expenditures 2022 (numeric) | float | 0% | 1 | 1.1 |
military_expenditures_military_expenditures_2021_text |
Military expenditures 2021 (text) | string | 0% | 1 | 1.2% of GDP (2021 est.) |
military_expenditures_military_expenditures_2021_numeric |
Military expenditures 2021 (numeric) | float | 0% | 1 | 1.2 |
military_expenditures_military_expenditures_2020_text |
Military expenditures 2020 (text) | string | 0% | 1 | 1.3% of GDP (2020 est.) |
military_expenditures_military_expenditures_2020_numeric |
Military expenditures 2020 (numeric) | float | 0% | 1 | 1.3 |
military_and_security_service_personnel_strengths_text |
military_and_security_service_personnel_strengths_text | string | 0% | 1 | information varies; approximately 170,000 active Armed... |
military_and_security_service_personnel_strengths_numeric |
military_and_security_service_personnel_strengths_numeric | float | 0% | 1 | 170000.0 |
military_equipment_inventories_and_acquisitions_text |
military_equipment_inventories_and_acquisitions_text | string | 0% | 1 | much of the military's inventory is comprised of... |
military_equipment_inventories_and_acquisitions_numeric |
military_equipment_inventories_and_acquisitions_numeric | float | 0% | 1 | 2025.0 |
military_service_age_and_obligation_text |
military_service_age_and_obligation_text | string | 0% | 1 | varies by service, but generally 17-23 for voluntary... |
military_service_age_and_obligation_numeric |
military_service_age_and_obligation_numeric | float | 0% | 1 | 17.0 |
military_deployments_text |
military_deployments_text | string | 0% | 1 | approximately 1,400 Central African Republic (MINUSCA);... |
military_deployments_numeric |
military_deployments_numeric | float | 0% | 1 | 1400.0 |
military_note_text |
military_note_text | string | 0% | 1 | the military’s primary responsibility is external... |
military_note_numeric |
military_note_numeric | float | 0% | 1 | 2024.0 |
source_section |
source_section | string | 0% | 1 | Military and Security |
source_profile_path |
source_profile_path | string | 0% | 1 | south-asia/bg.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
population_total_numeric |
Population count | float | SEL | 0% | 1 | 174370536.0 |
population_male_numeric |
Population male | float | SEL | 0% | 1 | 83908720.0 |
population_female_numeric |
Population female | float | SEL | 0% | 1 | 90461816.0 |
age_structure_0_14_years_numeric |
Population 0 14 percent | float | SEL | 0% | 1 | 25.1 |
age_structure_15_64_years_numeric |
Population 15 64 percent | float | SEL | 0% | 1 | 67.1 |
age_structure_65_years_and_over_numeric |
Population 65 plus percent | float | SEL | 0% | 1 | 7.8 |
dependency_ratios_total_dependency_ratio_numeric |
Total dependency ratio | float | SEL | 0% | 1 | 54.6 |
dependency_ratios_youth_dependency_ratio_numeric |
Youth dependency ratio | float | SEL | 0% | 1 | 43.5 |
dependency_ratios_elderly_dependency_ratio_numeric |
Elderly dependency ratio | float | SEL | 0% | 1 | 11.1 |
median_age_total_numeric |
Median age | float | SEL | 0% | 1 | 27.8 |
population_growth_rate_numeric |
Population growth rate percent | float | SEL | 0% | 1 | 0.91 |
birth_rate_numeric |
Birth rate per 1000 | float | SEL | 0% | 1 | 19.45 |
death_rate_numeric |
Death rate per 1000 | float | SEL | 0% | 1 | 6.05 |
net_migration_rate_numeric |
Net migration per 1000 | float | SEL | 0% | 1 | -4.28 |
urbanization_urban_population_numeric |
Urban population percent | float | SEL | 0% | 1 | 40.5 |
urbanization_rate_of_urbanization_numeric |
Urban growth rate percent | float | SEL | 0% | 1 | 2.88 |
sex_ratio_at_birth_numeric |
Sex ratio at birth | float | SEL | 0% | 1 | 1.04 |
sex_ratio_total_population_numeric |
Sex ratio overall | float | SEL | 0% | 1 | 0.96 |
maternal_mortality_ratio_numeric |
Maternal mortality per 100k | float | SEL | 0% | 1 | 115.0 |
infant_mortality_rate_total_numeric |
Infant mortality per 1000 | float | SEL | 0% | 1 | 22.0 |
life_expectancy_at_birth_total_population_numeric |
Life expectancy | float | SEL | 0% | 1 | 75.2 |
total_fertility_rate_numeric |
Fertility rate | float | SEL | 0% | 1 | 2.25 |
gross_reproduction_rate_numeric |
Gross reproduction rate | float | SEL | 0% | 1 | 1.1 |
physician_density_numeric |
Physicians per 1000 | float | SEL | 0% | 1 | 0.72 |
hospital_bed_density_numeric |
Hospital beds per 1000 | float | SEL | 0% | 1 | 0.9 |
literacy_total_population_numeric |
Literacy rate percent | float | SEL | 0% | 1 | 79.0 |
country_code |
Country code | string | SEL | 0% | 1 | BGD |
country_name |
Country name | string | SEL | 0% | 1 | Bangladesh |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
population_total_text |
population_total_text | string | 0% | 1 | 174,370,536 (2025 est.) |
population_male_text |
population_male_text | string | 0% | 1 | 83,908,720 |
population_female_text |
population_female_text | string | 0% | 1 | 90,461,816 |
age_structure_0_14_years_text |
age_structure_0_14_years_text | string | 0% | 1 | 25.1% (male 21,540,493/female 20,800,712) |
age_structure_15_64_years_text |
age_structure_15_64_years_text | string | 0% | 1 | 67.1% (male 55,071,592/female 58,180,322) |
age_structure_65_years_and_over_text |
age_structure_65_years_and_over_text | string | 0% | 1 | 7.8% (2024 est.) (male 6,096,167/female 7,007,898) |
dependency_ratios_total_dependency_ratio_text |
dependency_ratios_total_dependency_ratio_text | string | 0% | 1 | 54.6 (2025 est.) |
dependency_ratios_youth_dependency_ratio_text |
dependency_ratios_youth_dependency_ratio_text | string | 0% | 1 | 43.5 (2025 est.) |
dependency_ratios_elderly_dependency_ratio_text |
dependency_ratios_elderly_dependency_ratio_text | string | 0% | 1 | 11.1 (2025 est.) |
dependency_ratios_potential_support_ratio_text |
dependency_ratios_potential_support_ratio_text | string | 0% | 1 | 9 (2025 est.) |
dependency_ratios_potential_support_ratio_numeric |
dependency_ratios_potential_support_ratio_numeric | float | 0% | 1 | 9.0 |
median_age_total_text |
median_age_total_text | string | 0% | 1 | 27.8 years (2025 est.) |
median_age_male_text |
median_age_male_text | string | 0% | 1 | 28.7 years |
median_age_male_numeric |
median_age_male_numeric | float | 0% | 1 | 28.7 |
median_age_female_text |
median_age_female_text | string | 0% | 1 | 30.4 years |
median_age_female_numeric |
median_age_female_numeric | float | 0% | 1 | 30.4 |
population_growth_rate_text |
population_growth_rate_text | string | 0% | 1 | 0.91% (2025 est.) |
birth_rate_text |
birth_rate_text | string | 0% | 1 | 19.45 births/1,000 population (2025 est.) |
death_rate_text |
death_rate_text | string | 0% | 1 | 6.05 deaths/1,000 population (2025 est.) |
net_migration_rate_text |
net_migration_rate_text | string | 0% | 1 | -4.28 migrant(s)/1,000 population (2025 est.) |
urbanization_urban_population_text |
urbanization_urban_population_text | string | 0% | 1 | 40.5% of total population (2023) |
urbanization_rate_of_urbanization_text |
urbanization_rate_of_urbanization_text | string | 0% | 1 | 2.88% annual rate of change (2020-25 est.) |
major_urban_areas_population_text |
major_urban_areas_population_text | string | 0% | 1 | 23.210 million DHAKA (capital), 5.380 million... |
major_urban_areas_population_numeric |
major_urban_areas_population_numeric | float | 0% | 1 | 23.21 |
sex_ratio_at_birth_text |
sex_ratio_at_birth_text | string | 0% | 1 | 1.04 male(s)/female |
sex_ratio_0_14_years_text |
sex_ratio_0_14_years_text | string | 0% | 1 | 1.04 male(s)/female |
sex_ratio_0_14_years_numeric |
sex_ratio_0_14_years_numeric | float | 0% | 1 | 1.04 |
sex_ratio_15_64_years_text |
sex_ratio_15_64_years_text | string | 0% | 1 | 0.95 male(s)/female |
sex_ratio_15_64_years_numeric |
sex_ratio_15_64_years_numeric | float | 0% | 1 | 0.95 |
sex_ratio_65_years_and_over_text |
sex_ratio_65_years_and_over_text | string | 0% | 1 | 0.87 male(s)/female |
| +90 more extension fields — download the CSV/Parquet to see them all. | |||||
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BGD |
country_name |
Country name | string | SEL | 0% | 1 | Bangladesh |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
composition_religion_muslim_pct_synth |
Muslim | numeric | CCL | 0% | - | 91.0 |
composition_religion_hindu_pct_synth |
Hindu | numeric | CCL | 0% | - | 8.0 |
composition_religion_other_pct_synth |
other | numeric | CCL | 0% | - | 1.0 |
composition_ethnicity_primary_label_synth |
Bengali at least | string | CCL | 0% | - | Bengali at least |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
religions_text |
religions_text | string | 0% | 1 | Muslim 91%, Hindu 8%, other 1% (2022 est.) |
religions_numeric |
religions_numeric | float | 0% | 1 | 91.0 |
source_section |
source_section | string | 0% | 1 | People and Society:religions |
source_profile_path |
source_profile_path | string | 0% | 1 | south-asia/bg.json |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
composition_ethnicity_bengali_at_least_pct_synth |
Bengali at least | numeric | 0% | - | 99.0 |
composition_ethnicity_other_indigenous_ethnic_groups_pct_synth |
other indigenous ethnic groups | numeric | 0% | - | 1.0 |
composition_ethnicity_primary_share_pct_synth |
Bengali at least | numeric | 0% | - | 99.0 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BGD |
country_name |
Country name | string | SEL | 0% | 1 | Bangladesh |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
space_agency_agencies_text |
space_agency_agencies_text | string | 0% | 1 | Bangladesh Space Research and Remote Sensing... |
space_agency_agencies_numeric |
space_agency_agencies_numeric | float | 0% | 1 | 1991.0 |
space_program_overview_text |
space_program_overview_text | string | 0% | 1 | has a modest space program focused on designing,... |
space_program_overview_numeric |
space_program_overview_numeric | float | 0% | 1 | 2017.0 |
key_space_program_milestones_text |
key_space_program_milestones_text | string | 0% | 1 | 2017 - first educational/scientific nanosatellite... |
key_space_program_milestones_numeric |
key_space_program_milestones_numeric | float | 0% | 1 | 2017.0 |
source_section |
source_section | string | 0% | 1 | Space |
source_profile_path |
source_profile_path | string | 0% | 1 | south-asia/bg.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BGD |
country_name |
Country name | string | SEL | 0% | 1 | Bangladesh |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
terrorist_group_s_text |
terrorist_group_s_text | string | 0% | 1 | Harakat ul-Jihad-i-Islami/Bangladesh (HUJI-B); Islamic... |
source_section |
source_section | string | 0% | 1 | Terrorism |
source_profile_path |
source_profile_path | string | 0% | 1 | south-asia/bg.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
civil_aircraft_registration_country_code_prefix_text |
Civil aircraft registration country code prefix text | string | SEL+ | 0% | 1 | S2 |
airports_numeric |
Airports count | float | SEL | 0% | 1 | 17.0 |
country_code |
Country code | string | SEL | 0% | 1 | BGD |
country_name |
Country name | string | SEL | 0% | 1 | Bangladesh |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
civil_aircraft_registration_country_code_prefix_numeric |
civil_aircraft_registration_country_code_prefix_numeric | float | 0% | 1 | 2.0 |
airports_text |
airports_text | string | 0% | 1 | 17 (2025) |
heliports_text |
heliports_text | string | 0% | 1 | 36 (2025) |
heliports_numeric |
heliports_numeric | float | 0% | 1 | 36.0 |
railways_total_text |
railways_total_text | string | 0% | 1 | 2,460 km (2014) |
railways_total_numeric |
railways_total_numeric | float | 0% | 1 | 2460.0 |
railways_narrow_gauge_text |
railways_narrow_gauge_text | string | 0% | 1 | 1,801 km (2014) 1.000-m gauge |
railways_narrow_gauge_numeric |
railways_narrow_gauge_numeric | float | 0% | 1 | 1801.0 |
railways_broad_gauge_text |
railways_broad_gauge_text | string | 0% | 1 | 659 km (2014) 1.676-m gauge |
railways_broad_gauge_numeric |
railways_broad_gauge_numeric | float | 0% | 1 | 659.0 |
merchant_marine_total_text |
merchant_marine_total_text | string | 0% | 1 | 558 (2023) |
merchant_marine_total_numeric |
merchant_marine_total_numeric | float | 0% | 1 | 558.0 |
merchant_marine_by_type_text |
merchant_marine_by_type_text | string | 0% | 1 | bulk carrier 68, container ship 10, general cargo 170,... |
merchant_marine_by_type_numeric |
merchant_marine_by_type_numeric | float | 0% | 1 | 68.0 |
ports_total_ports_text |
ports_total_ports_text | string | 0% | 1 | 2 (2024) |
ports_total_ports_numeric |
ports_total_ports_numeric | float | 0% | 1 | 2.0 |
ports_large_text |
ports_large_text | float | 0% | 1 | 0 |
ports_large_numeric |
ports_large_numeric | float | 0% | 1 | 0.0 |
ports_medium_text |
ports_medium_text | float | 0% | 1 | 1 |
ports_medium_numeric |
ports_medium_numeric | float | 0% | 1 | 1.0 |
ports_small_text |
ports_small_text | float | 0% | 1 | 1 |
ports_small_numeric |
ports_small_numeric | float | 0% | 1 | 1.0 |
ports_very_small_text |
ports_very_small_text | float | 0% | 1 | 0 |
ports_very_small_numeric |
ports_very_small_numeric | float | 0% | 1 | 0.0 |
ports_ports_with_oil_terminals_text |
Ports with oil terminals (text) | float | 0% | 1 | 0 |
ports_ports_with_oil_terminals_numeric |
Ports with oil terminals (numeric) | float | 0% | 1 | 0.0 |
ports_key_ports_text |
ports_key_ports_text | string | 0% | 1 | Chittagong, Mongla |
source_section |
source_section | string | 0% | 1 | Transportation |
source_profile_path |
source_profile_path | string | 0% | 1 | south-asia/bg.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
country_code | string | SEL | 0% | 1 | BGD, BGD, BGD, BGD, BGD |
gns_language_code |
gns_language_code | string | CCL | 0% | 7 | ben, eng, syl, mya, zho |
gns_language_name |
gns_language_name | string | CCL | 0% | 7 | Bengali, English, Sylheti, Burmese, Chinese |
gns_toponym_count |
gns_toponym_count | integer | CCL | 0% | 3 | 33478, 91, 1, 1, 1 |
gns_toponym_share_pct |
gns_toponym_share_pct | float | CCL | 0% | 3 | 99.7141, 0.271, 0.003, 0.003, 0.003 |
gns_non_roman_toponym_count |
gns_non_roman_toponym_count | integer | CCL | 0% | 2 | 18, 0, 0, 0, 0 |
gns_dominant_script_code |
gns_dominant_script_code | string | CCL | 0% | 2 | Beng, , , , |
gns_dominant_script_name |
gns_dominant_script_name | string | CCL | 0% | 2 | Bengali, , , , |
gns_script_count |
gns_script_count | integer | CCL | 0% | 2 | 1, 0, 0, 0, 0 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
country_code | string | SEL | 0% | 1 | BGD |
admin_level |
admin_level | integer | SEL | 0% | 1 | 0 |
gns_country_name |
gns_country_name | string | SEL | 0% | 1 | Bangladesh |
gns_language_count |
gns_language_count | integer | CCL | 0% | 1 | 7 |
gns_script_count |
gns_script_count | integer | CCL | 0% | 1 | 1 |
gns_endonym_share_pct |
gns_endonym_share_pct | float | CCL | 0% | 1 | 99.9922 |
gns_non_roman_name_count |
gns_non_roman_name_count | integer | CCL | 0% | 1 | 18 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
gns_name_count |
gns_name_count | integer | 0% | 1 | 64383 |
gns_feature_count |
gns_feature_count | integer | 0% | 1 | 54408 |
gns_endonym_count |
gns_endonym_count | integer | 0% | 1 | 64378 |
gns_exonym_count |
gns_exonym_count | integer | 0% | 1 | 5 |
gns_source_build_date |
gns_source_build_date | string | 0% | 1 | Wed, 05 Aug 2026 |
gns_source_change_date |
gns_source_change_date | string | 0% | 1 | 2026-08-05 |
gns_name_count_populated_places |
gns_name_count_populated_places | integer | 0% | 1 | 57933 |
gns_feature_count_populated_places |
gns_feature_count_populated_places | integer | 0% | 1 | 49699 |
gns_name_count_hydrographic |
gns_name_count_hydrographic | integer | 0% | 1 | 4468 |
gns_feature_count_hydrographic |
gns_feature_count_hydrographic | integer | 0% | 1 | 3184 |
gns_name_count_administrative_regions |
gns_name_count_administrative_regions | integer | 0% | 1 | 224 |
gns_feature_count_administrative_regions |
gns_feature_count_administrative_regions | integer | 0% | 1 | 83 |
gns_name_count_spot_features |
gns_name_count_spot_features | integer | 0% | 1 | 975 |
gns_feature_count_spot_features |
gns_feature_count_spot_features | integer | 0% | 1 | 802 |
gns_name_count_hypsographic |
gns_name_count_hypsographic | integer | 0% | 1 | 346 |
gns_feature_count_hypsographic |
gns_feature_count_hypsographic | integer | 0% | 1 | 239 |
gns_name_count_areas_localities |
gns_name_count_areas_localities | integer | 0% | 1 | 405 |
gns_feature_count_areas_localities |
gns_feature_count_areas_localities | integer | 0% | 1 | 374 |
gns_name_count_vegetation |
gns_name_count_vegetation | integer | 0% | 1 | 24 |
gns_feature_count_vegetation |
gns_feature_count_vegetation | integer | 0% | 1 | 19 |
gns_name_count_transportation_networks |
gns_name_count_transportation_networks | integer | 0% | 1 | 8 |
gns_feature_count_transportation_networks |
gns_feature_count_transportation_networks | integer | 0% | 1 | 8 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BGD, BGD, BGD, BGD |
ethnic_group_name |
Ethnic group name | string | CCL | 0% | 4 | Bengali Muslims, Bengali Hindus, Tribal-Buddhists,... |
ethnic_power_status |
Ethnic power status | string | CCL | 0% | 2 | DOMINANT, DISCRIMINATED, DISCRIMINATED, DISCRIMINATED |
ethnic_population_share |
Ethnic population share | float | CCL | 0% | 4 | 0.895, 0.1, 0.01, 0.002 |
ethnic_group_id |
Ethnic group id | float | CCL | 0% | 4 | 77102000, 77101000, 77103000, 77104000 |
regional_autonomy |
Regional autonomy | string | CCL | 0% | 2 | , false, false, false |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
year |
year | integer | 0% | 1 | 2021, 2021, 2021, 2021 |
group_relevance |
group_relevance | string | 0% | 1 | , , , |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BGD, BGD |
society_id |
Society id | string | CCL | 0% | 2 | Ei10, Ei20 |
society_name |
Society name | string | CCL | 0% | 2 | Chakma, Kuki |
language_glottocode |
Language glottocode | string | CCL | 0% | 2 | chak1266, lush1249 |
language_name |
Language name | string | CCL | 0% | 1 | , |
kinship_system |
Kinship system | string | CCL | 0% | 2 | EA001:0; EA002:1; EA003:1; EA004:2; EA005:6, EA001:0;... |
marriage_pattern |
Marriage pattern | string | CCL | 0% | 2 | EA006:1; EA007:8; EA008:2; EA009:2; EA010:8, EA006:1;... |
subsistence_pattern |
Subsistence pattern | string | CCL | 0% | 2 | EA028:3; EA029:6; EA030:7; EA031:NA; EA032:2, EA028:NA;... |
political_complexity |
Political complexity | string | CCL | 0% | 2 | EA033:2; EA034:NA; EA035:NA, EA033:NA; EA034:NA; EA035:NA |
religion_importance |
Religion importance | string | CCL | 0% | 1 | EA034:NA; EA112:NA, EA034:NA; EA112:NA |
residence_pattern |
Residence pattern | string | CCL | 0% | 2 | EA011:1; EA012:8; EA013:2, EA011:1; EA012:8; EA013:9 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
country_name |
country_name | string | 0% | 1 | Bangladesh, Bangladesh |
dataset |
dataset | string | 0% | 1 | EA, EA |
region |
region | string | 0% | 1 | , |
latitude |
latitude | float | 0% | 1 | 23.0, 23.0 |
longitude |
longitude | float | 0% | 1 | 92.0, 92.0 |
assignment_method |
assignment_method | string | 0% | 1 | point_in_polygon, point_in_polygon |
assignment_confidence |
assignment_confidence | string | 0% | 1 | approximate, approximate |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
oc_anti_money_laundering |
oc_anti_money_laundering | numeric | CCL | 0% | 1 | - |
oc_arms_trafficking |
oc_arms_trafficking | numeric | CCL | 0% | 1 | - |
oc_criminal_actors |
oc_criminal_actors | numeric | CCL | 0% | 1 | - |
oc_criminal_markets |
oc_criminal_markets | numeric | CCL | 0% | 1 | - |
oc_criminality |
oc_criminality | numeric | CCL | 0% | 1 | - |
oc_cyber_dependent_crimes |
oc_cyber_dependent_crimes | numeric | CCL | 0% | 1 | - |
oc_financial_crimes |
oc_financial_crimes | numeric | CCL | 0% | 1 | - |
oc_human_smuggling |
oc_human_smuggling | numeric | CCL | 0% | 1 | - |
oc_human_trafficking |
oc_human_trafficking | numeric | CCL | 0% | 1 | - |
oc_judicial_system_and_detention |
oc_judicial_system_and_detention | numeric | CCL | 0% | 1 | - |
oc_law_enforcement |
oc_law_enforcement | numeric | CCL | 0% | 1 | - |
oc_political_leadership_and_governance |
oc_political_leadership_and_governance | numeric | CCL | 0% | 1 | - |
oc_resilience |
oc_resilience | numeric | CCL | 0% | 1 | - |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
oc_cannabis_trade |
oc_cannabis_trade | numeric | 0% | 1 | - |
oc_cocaine_trade |
oc_cocaine_trade | numeric | 0% | 1 | - |
oc_criminal_networks |
oc_criminal_networks | numeric | 0% | 1 | - |
oc_economic_regulatory_capacity |
oc_economic_regulatory_capacity | numeric | 0% | 1 | - |
oc_extortion_and_protection_racketeering |
oc_extortion_and_protection_racketeering | numeric | 0% | 1 | - |
oc_fauna_crimes |
oc_fauna_crimes | numeric | 0% | 1 | - |
oc_flora_crimes |
oc_flora_crimes | numeric | 0% | 1 | - |
oc_foreign_actors |
oc_foreign_actors | numeric | 0% | 1 | - |
oc_government_transparency_and_accountability |
oc_government_transparency_and_accountability | numeric | 0% | 1 | - |
oc_heroin_trade |
oc_heroin_trade | numeric | 0% | 1 | - |
oc_illicit_trade_in_excisable_goods |
oc_illicit_trade_in_excisable_goods | numeric | 0% | 1 | - |
oc_international_cooperation |
oc_international_cooperation | numeric | 0% | 1 | - |
oc_mafia_style_groups |
oc_mafia_style_groups | numeric | 0% | 1 | - |
oc_national_policies_and_laws |
oc_national_policies_and_laws | numeric | 0% | 1 | - |
oc_non_renewable_resource_crimes |
oc_non_renewable_resource_crimes | numeric | 0% | 1 | - |
oc_non_state_actors |
oc_non_state_actors | numeric | 0% | 1 | - |
oc_prevention |
oc_prevention | numeric | 0% | 1 | - |
oc_private_sector_actors |
oc_private_sector_actors | numeric | 0% | 1 | - |
oc_state_embedded_actors |
oc_state_embedded_actors | numeric | 0% | 1 | - |
oc_synthetic_drug_trade |
oc_synthetic_drug_trade | numeric | 0% | 1 | - |
oc_territorial_integrity |
oc_territorial_integrity | numeric | 0% | 1 | - |
oc_trade_in_counterfeit_goods |
oc_trade_in_counterfeit_goods | numeric | 0% | 1 | - |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
iso3 |
iso3 | string | 0% | 1 | BGD |
source |
source | string | 0% | 1 | Global Organized Crime Index |
source_url |
source_url | string | 0% | 1 | https://ocindex.net/ |
year |
year | integer | 0% | 1 | 2025 |
license |
license | string | 0% | 1 | Creative Commons (GI-TOC / ENACT) |
oc_anti_money_laundering_rank |
oc_anti_money_laundering_rank | integer | 0% | 1 | 98 |
oc_anti_money_laundering_2023 |
oc_anti_money_laundering_2023 | float | 0% | 1 | 5.5 |
oc_anti_money_laundering_2021 |
oc_anti_money_laundering_2021 | float | 0% | 1 | 4.5 |
oc_arms_trafficking_rank |
oc_arms_trafficking_rank | integer | 0% | 1 | 79 |
oc_arms_trafficking_2023 |
oc_arms_trafficking_2023 | integer | 0% | 1 | 5 |
oc_arms_trafficking_2021 |
oc_arms_trafficking_2021 | integer | 0% | 1 | 5 |
oc_cannabis_trade_rank |
oc_cannabis_trade_rank | integer | 0% | 1 | 114 |
oc_cannabis_trade_2023 |
oc_cannabis_trade_2023 | integer | 0% | 1 | 4 |
oc_cannabis_trade_2021 |
oc_cannabis_trade_2021 | integer | 0% | 1 | 4 |
oc_cocaine_trade_rank |
oc_cocaine_trade_rank | integer | 0% | 1 | 137 |
oc_cocaine_trade_2023 |
oc_cocaine_trade_2023 | integer | 0% | 1 | 3 |
oc_cocaine_trade_2021 |
oc_cocaine_trade_2021 | integer | 0% | 1 | 3 |
oc_criminal_actors_rank |
oc_criminal_actors_rank | integer | 0% | 1 | 88 |
oc_criminal_actors_2023 |
oc_criminal_actors_2023 | float | 0% | 1 | 5.2 |
oc_criminal_actors_2021 |
oc_criminal_actors_2021 | integer | 0% | 1 | 5 |
oc_criminal_markets_rank |
oc_criminal_markets_rank | integer | 0% | 1 | 83 |
oc_criminal_markets_2023 |
oc_criminal_markets_2023 | float | 0% | 1 | 5.03 |
oc_criminal_markets_2021 |
oc_criminal_markets_2021 | float | 0% | 1 | 4.95 |
oc_criminal_networks_rank |
oc_criminal_networks_rank | integer | 0% | 1 | 74 |
oc_criminal_networks_2023 |
oc_criminal_networks_2023 | integer | 0% | 1 | 6 |
oc_criminal_networks_2021 |
oc_criminal_networks_2021 | float | 0% | 1 | 5.5 |
oc_criminality_rank |
oc_criminality_rank | integer | 0% | 1 | 83 |
oc_criminality_2023 |
oc_criminality_2023 | float | 0% | 1 | 5.12 |
oc_criminality_2021 |
oc_criminality_2021 | float | 0% | 1 | 4.98 |
oc_cyber_dependent_crimes_rank |
oc_cyber_dependent_crimes_rank | integer | 0% | 1 | 79 |
| +74 more pending fields — download the CSV/Parquet to see them all. | |||||
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | BGD |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
wave |
wave | integer | 0% | 1 | 7 |
wave_years |
wave_years | string | 0% | 1 | 2017-2022 |
source |
source | string | 0% | 1 | World Values Survey |
importance_religion |
importance_religion | integer | 0% | 1 | 1 |
importance_family |
importance_family | integer | 0% | 1 | 1 |
importance_friends |
importance_friends | integer | 0% | 1 | 1 |
trust_people |
trust_people | integer | 0% | 1 | 1 |
trust_family |
trust_family | integer | 0% | 1 | 1 |
life_satisfaction |
life_satisfaction | integer | 0% | 1 | 1 |
happiness |
happiness | integer | 0% | 1 | 1 |
freedom_choice |
freedom_choice | integer | 0% | 1 | 1 |
gender_jobs_scarce |
gender_jobs_scarce | integer | 0% | 1 | 1 |
gender_political_leaders |
gender_political_leaders | integer | 0% | 1 | 1 |
gender_university |
gender_university | integer | 0% | 1 | 1 |
justifiable_divorce |
justifiable_divorce | integer | 0% | 1 | 1 |
justifiable_homosexuality |
justifiable_homosexuality | integer | 0% | 1 | 1 |
immigration_policy |
immigration_policy | integer | 0% | 1 | 1 |
immigrants_jobs |
immigrants_jobs | integer | 0% | 1 | 1 |
immigrants_culture |
immigrants_culture | integer | 0% | 1 | 1 |
confidence_government |
confidence_government | integer | 0% | 1 | 1 |
confidence_parliament |
confidence_parliament | integer | 0% | 1 | 1 |
confidence_police |
confidence_police | integer | 0% | 1 | 1 |
confidence_courts |
confidence_courts | integer | 0% | 1 | 1 |
confidence_press |
confidence_press | integer | 0% | 1 | 1 |
democracy_importance |
democracy_importance | integer | 0% | 1 | 1 |
Which languages name the landscape here, and in which writing systems. A language's toponymic footprint and its speaker population are different measures and often diverge. Counts include variant and foreign-language renderings of the same place, so a language can rank high because outside sources record names in it rather than because it is spoken locally — and a widely spoken language can be almost absent where official naming is in another language.
| Language | Place names | Share | Script |
|---|---|---|---|
| Bengali (ben) | 33,478 | 99.7% | Bengali |
| English (eng) | 91 | 0.3% | — |
54,408 distinct features ·
7 languages ·
1 script ·
18 names in non-Roman script ·
5 conventional English names
Source: NGA GEOnet Names Server
(public domain) · rebuilt Wed, 05 Aug 2026.
Names follow the US/BGN convention.
The data providers this country's datasets are sourced from — each links out to the provider.
Pick datasets and admin level(s). You'll get a .zip with one CSV per dataset, each filtered to the levels you choose.